GGML_OP_RESHAPE, GGML_OP_VIEW, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE, along with GGML_OP_NONE, are all noops in ggml. I.e., nothing happens. But ggml still has a thread barrier after them, which wastes time. The waste is not too bad for large models where computations are long compared to the time taken for thread synchronization. But for small models skipping those unnecessary waits makes a noticeable difference.
Let's look at a really tiny model - the 99M parameter TriLM ternary model quantized with IQ2_TN. The following table compares performance for PP-512 and TG-128 with and without the change in this PR
CPU
threads
test
t/s (main)
t/s (PR)
Speedup
Ryzen-7950X
16
pp512
11386.75 ± 19.08
11587.58 ± 34.26
1.018
Ryzen-7950X
8
tg128
1312.25 ± 1.02
1460.80 ± 1.69
1.113
M2-Max
8
pp512
7642.81 ± 22.07
7680.29 ± 9.29
1.005
M2-Max
8
tg128
992.83 ± 18.17
1096.47 ± 14.45
1.104
So, basically, for such a small model ggml spends 10% of its time waiting for threads to pass through a barrier after a noop when generating tokens.
There are other barriers that can be eliminated. E.g., the typical attention block involves matrix multiplications of the Q, K and V tensors with the same activations, so there is no need to synchronize threads after each such matrix multiplications. In a similar way, in the feed-forward portion of the network the ffn_up and ffn_gate tensors multiply the same activations, so one can save another barrier there. This is left for a future PR.
GGML_OP_RESHAPE, GGML_OP_VIEW, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE, along withGGML_OP_NONE, are all noops inggml. I.e., nothing happens. Butggmlstill has a thread barrier after them, which wastes time. The waste is not too bad for large models where computations are long compared to the time taken for thread synchronization. But for small models skipping those unnecessary waits makes a noticeable difference.Let's look at a really tiny model - the 99M parameter TriLM ternary model quantized with
IQ2_TN. The following table compares performance for PP-512 and TG-128 with and without the change in this PRSo, basically, for such a small model
ggmlspends 10% of its time waiting for threads to pass through a barrier after a noop when generating tokens.There are other barriers that can be eliminated. E.g., the typical attention block involves matrix multiplications of the
Q, KandVtensors with the same activations, so there is no need to synchronize threads after each such matrix multiplications. In a similar way, in the feed-forward portion of the network theffn_upandffn_gatetensors multiply the same activations, so one can save another barrier there. This is left for a future PR.